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Found 8 papers, 4 papers with code
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
The misuse of large language models (LLMs) has garnered significant attention from the general public and LLM vendors.
Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models
Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world.
In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT
In this paper, we perform the first large-scale measurement of ChatGPT's reliability in the generic QA scenario with a carefully curated set of 5, 695 questions across ten datasets and eight domains.
MGTBench: Benchmarking Machine-Generated Text Detection
Nonetheless, we note that only a small fraction of adversarial-crafted perturbations on MGTs can evade the ChatGPT Detector, thus highlighting the need for more robust MGT detection methods.
Prompt Stealing Attacks Against Text-to-Image Generation Models
In this paper, we propose a novel attack, namely prompt stealing attack, which aims to steal prompts from generated images by text-to-image generation models.
Backdoor Attacks in the Supply Chain of Masked Image Modeling
Different from previous work, we are the first to systematically threat modeling on SSL in every phase of the model supply chain, i. e., pre-training, release, and downstream phases.
Nonconvex Sparse Logistic Regression with Weakly Convex Regularization
In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem.
Disciplined Multi-Convex Programming
A multi-convex optimization problem is one in which the variables can be partitioned into sets over which the problem is convex when the other variables are fixed.
Optimization and Control
